Method of detecting, identifying and correcting process performance

ABSTRACT

A method for material processing utilizing a material processing system ( 1 ) to perform a process. The method a process, measures a scan of data, and transforms the data scan into a signature including at least one spatial component. The scan of data can include a process performance parameter ( 14 ) such as an etch rate, an etch selectivity, a deposition rate, a film property, etc. A relationship can be determined between the measured signature and a set of at least one controllable process parameter ( 12 ) using multivariate analysis, and this relationship can be utilized to improve the scan of data corresponding to a process performance parameter. For example, utilizing this relationship to minimize the spatial components of the scan of data can affect an improvement in the process uniformity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority and is related to U.S. application Ser.No. 60/343,174, filed on Dec. 31, 2001, the contents of which are hereinincorporated by reference. This application is related to co-pending PCTapplication serial no. PCT/US02/XXXXX, filed on even date herewith,Attorney Docket No. 216952WO, the contents of which are hereinincorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention relates to material processing and moreparticularly to a method for detecting, identifying and correctingmaterial processing performance.

2. Description of Related Art

One area of material processing in the semiconductor industry whichpresents formidable challenges is, for example, the manufacture ofintegrated circuits (ICs). Demands for increasing the speed of ICs ingeneral, and memory devices in particular, force semiconductormanufacturers to make devices smaller and smaller on the wafer surface.And conversely, while shrinking device sizes on the substrate isincurred, the number of devices fabricated on a single substrate isdramatically increased with further expansion of the substrate diameter(or processing real estate) from 200 mm to 300 mm and greater. Both thereduction in feature size, which places greater emphasis on criticaldimensions (CD), and the increase of substrate size lead to even greaterrequirements on material processing uniformity to maximize the yield ofsuperior devices.

Typically, during materials processing, one method to facilitate theaddition and removal of material films when fabricating compositematerial structures includes, for example, the use of plasma. Forexample, in semiconductor processing, a (dry) plasma etch process isutilized to remove or etch material along fine lines or within vias orcontacts patterned on a silicon substrate.

During, for example, material processing in IC fabrication, shrinkingcritical feature sizes, increasing substrate sizes and escalatingnumbers and complexities of processes lead to the necessity to controlthe material processing uniformity throughout the lifetime of a processand from process-to-process. The lack of uniformity in simply oneprocess measurable generally requires the sacrifice of other importantprocess parameters, at least, somewhere during the process. In materialprocessing, the lack of process uniformity can, for example, cause acostly reduction in the yield of superior devices.

Attempts to design material processing hardware either to produceuniform processing properties or correct for known non-uniformities arefurther complicated by the expansive set of independent parameters, thecomplexity of these material processing devices, and simply theexorbitant cost and lack of robustness of such material processingdevices. Furthermore, for conventional material processing devices, thenumber of externally, controllable parameters are severely limited toonly a few known, adjustable parameters. Therefore, it is essential thatthe inter-relations between all externally controllable parameters andmeasurable process parameters are derived and made useful throughout thelifetime of a process and from process-to-process.

SUMMARY OF THE INVENTION

The present invention provides for a method of characterizing a materialprocessing system that comprises a process chamber, a device formeasuring and adjusting at least one controllable process parameter, anda device for measuring at least one process performance parameter.

The present invention provides a method comprising the steps of varyinga controllable process parameter associated with a process performed inthe material processing system, measuring a scan of data, transformingthe scan of data into a number of spatial components, and characterizingthe material processing system by identifying a process signature, theprocess signature comprising at least one of the spatial components.

The present invention also provides a method further comprising thesteps of varying an additional controllable process parameter, measuringan additional scan of data, transforming the additional scan of datainto an additional number of spatial components, and re-characterizingthe material processing system by including the additional processsignature comprising the additional number of spatial components.

In addition, the present invention provides a method further comprisingthe steps of determining a relationship between the process signatureand a controllable process parameter, and adjusting the controllableprocess parameter, wherein the adjusting comprises utilizing therelationship between the process signature and the controllable processparameter to affect an improvement to the scan of data.

Also, the present invention provides a method further comprising thesteps of determining inter-relationships between the variations in thecontrollable process parameters and the spatial components usingmultivariate analysis, and adjusting at least one controllable processparameter, wherein the adjusting comprises utilizing theinter-relationships to affect an improvement to the process.

Furthermore, the present invention provides a method further comprisingthe steps of comparing the process signature with an ideal signature forthe process, wherein the comparing comprises determining a differencesignature, and minimizing the difference signature by adjusting thecontrollable process parameter, wherein the adjusting comprisesutilizing the relationship between the process signature and thecontrollable process parameter.

Moreover, the present invention provides a method further comprising thesteps of comparing the data matrix with an ideal matrix for the materialprocessing system, wherein comparing comprises determining at least onedifference signature, determining at least one inter-relationshipbetween a difference signature and at least one controllable processparameter, and minimizing the difference signature by adjusting the atleast one controllable process parameter, wherein the adjustingcomprises utilizing the at least one inter-relationship betweendifference signature and the at least one controllable processparameter.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other advantages of the invention will become more apparentand more readily appreciated from the following detailed description ofthe exemplary embodiments of the invention taken in conjunction with theaccompanying drawings, where:

FIG. 1 shows a material processing system according to a preferredembodiment of the present invention;

FIG. 2 shows a material processing system according to an alternateembodiment of the present invention;

FIG. 3 shows a material processing system according to anotherembodiment of the present invention;

FIG. 4 shows a material processing system according to anotherembodiment of the present invention;

FIG. 5 shows a material processing system according to an additionalembodiment of the present invention;

FIG. 6A presents a data scan of a first etch rate profile;

FIG. 6B presents a spectrum of spatial components for the data scan ofFIG. 6A;

FIG. 7A presents a data scan of a second etch rate profile;

FIG. 7B presents a spectrum of spatial components for the data scan ofFIG. 7A;

FIG. 8A presents a comparison of the spectrum of spatial componentsresulting from an increase in process pressure;

FIG. 8B presents the difference spectrum for the data of FIG. 8A;

FIG. 9A presents a comparison of the spectrum of spatial componentsresulting from a decrease in RF power;

FIG. 9B presents the difference spectrum for the data of FIG. 9A;

FIG. 9C presents the difference spectrum for an increase in RF power;

FIG. 10A shows an exemplary spectrum of spatial components for anon-uniform etch rate;

FIG. 10B shows an exemplary spectrum of spatial components for a uniformetch rate;

FIG. 11 presents an exemplary table of variations in spatial componentsprovided changes in controllable process parameters;

FIG. 12 presents an exemplary plot of the cumulative sum of squares andcumulative sum of variations to the sum of squares for three principalcomponents;

FIG. 13A presents the scores corresponding to each spatial component int(1), t(2) space provided the exemplary data of FIG. 11;

FIG. 13B presents the loadings for each variable in p(1), p(2) spaceprovided the exemplary data of FIG. 11;

FIG. 14A presents the scores corresponding to each spatial component int(1), t(3) space provided the exemplary data of FIG. 11;

FIG. 14B presents the loadings for each variable in p(1), p(3) spaceprovided the exemplary data of FIG. 11;

FIG. 15 presents an exemplary table summarizing data presented in FIGS.13A,B and 14A,B;

FIG. 16A presents a table of spatial components for a reduced set of thedata of the table presented in FIG. 11;

FIG. 16B presents a spectrum of spatial components according to the dataof FIGs. 6A,B, and a spectrum of spatial components according to thedata of FIG. 16A;

FIG. 16C presents a difference spectrum obtained from the spectra ofFIG. 16B;

FIG. 17 shows a data scan of a first etch profile according to the dataof FIGS. 6A,B, and a data scan of a second etch profile according to thedata of FIG. 16C;

FIG. 18A presents a flow diagram of a method according to the presentinvention;

FIG. 18B presents a flow diagram of an additional method according tothe present invention; and

FIG. 18C presents a flow diagram of an additional method according tothe present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

According to an embodiment of the present invention, a materialprocessing system 1 is depicted in FIG. 1 comprising a process chamber10, a device for measuring and adjusting at least one controllableprocess parameter 12, a device for measuring at least processperformance parameter 14, and a controller 55. The controller 55 iscoupled to the device for measuring and adjusting at least onecontrollable process parameter 12 and the device for measuring at leastone process performance parameter. Moreover, the controller 55 iscapable of executing the method of performing a process to be described.

In the illustrated embodiment, material processing system 1, depicted inFIG. 1, utilizes a plasma for material processing. Desirably, materialprocessing system 1 comprises an etch chamber. Alternately, materialprocessing system 1 comprises a photoresist coating chamber such as, forexample, a photoresist spin coating system. In another embodiment,material processing system 1 comprises a photoresist patterning chambersuch as, for example, a ultraviolet (UV) lithography system. In anotherembodiment, material processing system 1 comprises a dielectric coatingchamber such as, for example, a spin-on-glass (SOG) orspin-on-dielectric (SOD) system. In another embodiment, materialprocessing system 1 comprises a deposition chamber such as, for example,a chemical vapor deposition (CVD) system or a physical vapor deposition(PVD) system. In an additional embodiment, material processing system 1comprises a rapid thermal processing (RTP) chamber such as, for example,a RTP system for thermal annealing. In another embodiment, materialprocessing system 1 comprises a batch diffusion furnace.

According to the illustrated embodiment of the present inventiondepicted in FIG. 2, material processing system 1 can comprise processchamber 10, substrate holder 20, upon which a substrate 25 to beprocessed is affixed, gas injection system 40, and vacuum pumping system50. Substrate 25 can be, for example, a semiconductor substrate, a waferor a liquid crystal display. Process chamber 10 can be, for example,configured to facilitate the generation of plasma in processing region45 adjacent a surface of substrate 25, wherein plasma is formed viacollisions between heated electrons and an ionizable gas. An ionizablegas or mixture of gases is introduced via gas injection system 40 andthe process pressure is adjusted. For example, a control mechanism (notshown) can be used to throttle the vacuum pumping system 50. Desirably,plasma is utilized to create materials specific to a pre-determinedmaterials process, and to aid either the deposition of material tosubstrate 25 or the removal of material from the exposed surfaces ofsubstrate 25.

Substrate 25 can be, for example, transferred into and out of chamber 10through a slot valve (not shown) and chamber feed-through (not shown)via robotic substrate transfer system where it is received by substratelift pins (not shown) housed within substrate holder 20 and mechanicallytranslated by devices housed therein. Once substrate 25 is received fromsubstrate transfer system, it is lowered to an upper surface ofsubstrate holder 20.

Desirably, the substrate 25 can be, for example, affixed to thesubstrate holder 20 via an electrostatic clamping system 28.Furthermore, substrate holder 20 can further include a cooling systemincluding a re-circulating coolant flow that receives heat fromsubstrate holder 20 and transfers heat to a heat exchanger system (notshown), or when heating, transfers heat from the heat exchanger system.The heating/cooling system further comprises a device 27 for monitoringthe substrate 25 and/or substrate holder 20 temperature. The device 27can be, for example, a thermocouple (e.g. K-type thermocouple),pyrometer, or optical thermometer. Moreover, gas can be delivered to theback-side of the substrate via a backside gas system 26 to improve thegas-gap thermal conductance between substrate 25 and substrate holder20. Such a system can be utilized when temperature control of thesubstrate is required at elevated or reduced temperatures. For example,temperature control of the substrate can be useful at temperatures inexcess of the steady-state temperature achieved due to a balance of theheat flux delivered to the substrate 25 from the plasma and the heatflux removed from substrate 25 by conduction to the substrate holder 20.In other embodiments, heating elements, such as resistive heatingelements, or thermoelectric heaters/coolers can be included.

In the illustrated embodiment, shown in FIG. 2, substrate holder 20 can,for example, further serve as an electrode through which RF power iscoupled to plasma in processing region 45. For example, substrate holder20 is electrically biased at a RF voltage via the transmission of RFpower from RF generator 30 through impedance match network 32 tosubstrate holder 20. The RF bias can serve to heat electrons and,thereby, form and maintain plasma. In this configuration, the system canoperate as a reactive ion etch (RIE) reactor, wherein the chamber andupper gas injection electrode serve as ground surfaces. A typicalfrequency for the RF bias can range from 1 MHz to 100 MHz (e.g., 13.56MHz). RF systems for plasma processing are well known to those skilledin the art.

Alternately, RF power is applied to the substrate holder electrode atmultiple frequencies. Furthermore, impedance match network 32 serves tomaximize the transfer of RF power to plasma in processing chamber 10 byminimizing the reflected power. Match network topologies (e.g. L-type,π-type, T-type, etc.) and automatic control methods are well known tothose skilled in the art.

With continuing reference to FIG. 2, process gas 42 can be, for example,introduced to processing region 45 through gas injection system 40.Process gas 42 can, for example, comprise a mixture of gases such asargon, CF₄ and O₂, or argon, C₄F₈ and O₂ for oxide etch applications.Gas injection system 40 can comprise a showerhead, wherein process gas42 is supplied from a gas delivery system (not shown) to the processingregion 45 through a gas injection plenum (not shown), a series of baffleplates (not shown) and a multi-orifice showerhead gas injection plate(not shown). Gas injection systems are well known to those of skill inthe art.

Vacuum pump system 50 can, for example, include a turbo-molecular vacuumpump (TMP) capable of a pumping speed up to 5000 liters per second (andgreater) and a gate valve for throttling the chamber pressure. Inconventional plasma processing devices utilized for dry plasma etch, a1000 to 3000 liter per second TMP is employed. TMPs are useful for lowpressure processing, typically less than 50 mTory. At higher pressures,the TMP pumping speed falls off dramatically, For high pressureprocessing (i.e. greater than 100 mTorr), a mechanical booster pump anddry roughing pump can be used. Furthermore, a device for monitoringchamber pressure 52 is coupled to the chamber 10. The pressure measuringdevice 52 can be, for example, a Type 628B Baratron absolute capacitancemanometer commercially available from MKS Instruments, Inc. (Andover,Mass.).

Material processing system 1 further comprises a metrology tool 100 tomeasure process performance parameters such as, for example for etchsystems, an etch rate, an etch selectivity (i.e. ratio of etch rate ofone material to etch rate of a second material), an etch uniformity, afeature profile angle, a critical dimension, etc. The metrology tool 100can be either an in-situ or ex-situ device. For an in-situ device, themetrology tool 100 can be, for example, a scatterometer, incorporatingbeam profile ellipsometry and beam profile reflectometry, commerciallyavailable from Therma-Wave, Inc. (1250 Reliance Way, Fremont, Calif.94539) which is positioned within the transfer chamber (not shown) toanalyze substrates 25 transferred into and out of process chamber 10.For an ex-situ device, the metrology tool 100 can be, for example, ascanning electron microscope (SEM) wherein substrates have been cleavedand features are illuminated to determine the above performanceparameters. The latter approach is well known to those skilled in theart of substrate inspection. The metrology tool is further coupled tocontroller 55 to provide controller 55 with spatially resolvedmeasurements of the process performance parameters.

Controller 55 comprises a microprocessor, memory, and a digital I/O portcapable of generating control voltages sufficient to communicate andactivate inputs to material processing system 1 as well as monitoroutputs from material processing system 1. Moreover, controller 55 iscoupled to and exchanges information with RF generator 30, impedancematch network 32, gas injection system 40, vacuum pump system 50,pressure measuring device 52, backside gas delivery system 26,substrate/substrate holder temperature measurement system 27,electrostatic clamping system 28, and metrology tool 100. A programstored in the memory is utilized to activate the inputs to theaforementioned components of a material processing system 1 according toa stored process recipe. One example of controller 55 is a DELLPRECISION WORKSTATION 610™, available from Dell Corporation, Dallas,Tex.

In the illustrated embodiment, shown in FIG. 3, the material processingsystem 1 can, for example, further comprise either a mechanically orelectrically rotating dc magnetic field system 60, in order topotentially increase plasma density and/or improve plasma processinguniformity, in addition to those components described with reference toFIGS. I and 2. Moreover, controller 55 is coupled to rotating magneticfield system 60 in order to regulate the speed of rotation and fieldstrength. The design and implementation of a rotating magnetic field iswell known to those skilled in the art.

In the illustrated embodiment, shown in FIG. 4, the material processingsystem 1 of FIGS. 1 and 2 can, for example, further comprise an upperelectrode 70 to which RF power can be coupled from RF generator 72through impedance match network 74. A typical frequency for theapplication of RF power to the upper electrode can range from 10 MHz to200 MHz (e.g., 60 MHz). Additionally, a typical frequency for theapplication of power to the lower electrode can range from 0.1 MHz to 30MHz (e.g., 2 MHz). Moreover, controller 55 is coupled to RF generator 72and impedance match network 74 in order to control the application of RFpower to upper electrode 70. The design and implementation of an upperelectrode is well known to those skilled in the art.

In the illustrated embodiment, shown in FIG. 5, the material processingsystem of FIG. 1 can, for example, further comprise an inductive coil 80to which RF power is coupled via RP generator 82 through impedance matchnetwork 84. RF power is inductively coupled from inductive coil 80through dielectric window (not shown) to plasma processing region 45. Atypical frequency for the application of RF power to the inductive coil80 can range from 10 MHz to 100 MHz (e.g., 13.56 MHz). Similarly, atypical frequency for the application of power to the chuck electrodecan range from 0.1 MHz to 30 MHz (e.g., 13.56 MHz). In addition, aslotted Faraday shield (not shown) can be employed to reduce capacitivecoupling between the inductive coil 80 and plasma. Alternately, coil 80can be positioned above chamber 10 as a spiral-like coil such as in atransformer coupled plasma (TCP) source. Moreover, controller 55 iscoupled to RF generator 82 and impedance match network 84 in order tocontrol the application of power to inductive coil 80. The design andimplementation of an inductively coupled plasma (ICP) source and atransformer coupled plasma (TCP) source are well known to those skilledin the art.

Alternately, the plasma can be formed using electron cyclotron resonance(ECR). In yet another embodiment, the plasma is formed from thelaunching of a Helicon wave. In yet another embodiment, the plasma isformed from a propagating surface wave. Each plasma source describedabove is well known to those skilled in the art.

Referring now to FIGS. 1 through 5, substrates 25 are processed inprocess chamber 10 and some process performance parameters can bemeasured utilizing, for example, the metrology tool 100. Desirably,process performance parameters can include, for instance, etch rate,deposition rate, etch selectivity (ratio of the rate at which a firstmaterial is etched to the rate at which a second material is etched), anetch critical dimension (e.g. length or width of feature), an etchfeature anisotropy (e.g. etch feature sidewall profile), a film property(e.g. film stress, porosity, etc.), a plasma density (obtained, forexample, from a Langmuir probe), an ion energy (obtained, for example,from an ion energy spectrum analyzer), a concentration of a chemicalspecie (obtained, for example, from optical emission spectroscopy), atemperature, a pressure, a mask (e.g. photoresist) film thickness, amask (e.g. photoresist) pattern critical dimension, etc. For example,FIG. 6A presents a substrate scan of the etch rate (Angstroms/minute,A/min) as a function of position (millimeters, mm) on a first substrate25, where a position of zero (0) corresponds to the center of substrate25 and a position of plus or minus (±) 100 corresponds to diametricallyopposite edges of a, for example, (200 mm) substrate 25. Similarly, FIG.7A presents a substrate scan of the etch rate versus substrate positionfor a second substrate 25.

In FIGS. 6A and 7A, thirty-two (32) samples are taken along a fullradial scan (edge-to-edge) of the substrate diameter; however, ingeneral, the number of samples can be arbitrary, e.g. N samples whereN≧2. The time T required to input the data at a sampling rate R can beexpressed as T=N/R; i.e. T=N/R=(32 samples)/(1000 samples/second)=0.032seconds (for sampling 32 points across a substrate at 1 kHz). For a datascan of period T, the primary spatial component is f=1/T and the highestspatial component must satisfy the Nyquist critical frequency off_(max)≦1/2Δ, where Δ=T/N. Therefore, in the above example,f=1/T=R/N=31.25 Hz and f_(max)=1/2Δ=R/2=500 Hz.

In general, a scan of data, as described above, can be manipulated intospectral space and be represented by a set of orthogonal components. Forexample, if the samples are equally spaced in time (or space) and thescan is assumed to be periodic, then the data scan is directly amenableto the application of a discrete Fourier transform of the data toconvert the data from physical space to Fourier (spectral) space.Moreover, if the samples are unequally spaced in time (or space), thereexist methods of treating the data. These methods are known to thoseskilled in the art of data processing. When using a Fourier seriesrepresentation of the data, the spatial components can be, for example,Fourier harmonics. Moreover, if the sampling period T is relativelysmall (small relative to the change of the data scan in time; applicableonly for in-situ monitoring during substrate processing), then theFourier spectrum can be regarded as a wavenumber spectrum and theminimum and maximum spatial components can be referred to as the minimumand maximum wavenumbers (or maximum and minimum wavelengths,respectively).

FIG. 6B presents the amplitude of each spatial component (i.e.f_(n)=n/NΔ, n=1,N/2) for the data scan shown in FIG. 6A. And similarly,FIG. 7B presents the amplitude of each spatial component (i.e.f_(n)=n/NΔ, n=1,N/2) for the data scan shown in FIG. 7A. In general, thefollowing observations can be made: (1) the primary spatial component(f₁) has the largest magnitude and represents contributions from eachpoint in the data scan (therefore, all points are interdependent;longest wavelength); and (2) the highest spatial component (f_(N/2)) hastypically the smallest magnitude and it represents each point in thedata scan separately (therefore, all points are independent of oneanother; smallest wavelength). Additionally, subtle changes in the etchrate profile (i.e. FIG. 6A versus FIG. 7A) can have a significant effecton the signature described by the spatial components in spectral space(i.e. FIG. 6B versus 7B).

Therefore, changes in the signature (spectrum) of spatial components canindicate whether the process variations leading to the observed spectralshifts are occurring globally over the substrate or locally over thesubstrate. In summary, changes in the amplitudes of the lower orderspatial components (i.e. f₁, f₂, f₃, . . . ) reflect global variationsof processing parameters above substrate 25, and changes in theamplitudes of the higher order spatial components (i.e. . . .,f_(N/2-2), f_(N/2-1), f_(N/2)) reflect local variations of processingparameters above substrate 25.

For example, a change in the pressure or RF power (e.g. an increase inthe processing pressure or decrease in the RF power) is expected to havea global effect on the signature of spatial components and, hence,affect primarily the lower order components. FIG. 8A presents an exampleof raising the chamber pressure and its effect on the signature ofspatial components, where FIG. 8B presents the respective differencesignature. Similarly, FIG. 9A presents an example of reducing the RFpower and its effect on the signature of spatial components, where FIG.9B presents the respective difference signature (for reducing the RFpower) and FIG. 9C presents the corresponding difference signature forincreasing the RF power. Each difference signature provides a differentspatial characteristic (i.e. “fingerprint”) for each type of processchange (i.e. increase or decrease in process pressure, increase ordecrease in RF power, increase or decrease in mass flow rate of processgas, etc.).

Since each process performed in material processing system 1 can becharacterized by a signature of its spatial components, then one canevaluate the effect of process uniformity on the signature of spatialcomponents. FIG. 10A presents a spatial signature for a non-uniformprocess and FIG. 10B presents a spatial signature for a uniform process.Clearly, the uniformity of a process can be directly correlated with anoverall reduction in the magnitudes of each spatial component.

Since there exists a relationship between controllable processparameters and spectra of spatial components obtained from a scan of,for instance, the etch rate across the substrate, then it is conceivablethat spatial component differences can be subjected to linearsuperposition, i.e added and subtracted, to minimize the magnitudes ofall spatial components and, therefore, produce a uniform process. Amethod of establishing a correlation between changes in controllableprocess parameters and spatial components utilizing multivariateanalysis is now described to determine the right combination ofvariables to produce a uniform process.

The table provided in FIG. 11 presents the relative change in amplitudeof each spatial component through the first sixteen (16) components fortwelve (12) variations in controllable process parameters. Thecontrollable process parameters include, for example: (1) increase inprocess pressure, (2) decrease in process pressure, (3) increase in(Helium) backside gas pressure, (4) decrease in (Helium) backside gaspressure, (5) increase in CF₄ partial pressure, (6) decrease in CF₄partial pressure, (7) increase in RF power, (8) decrease in RF power,(9) increase in substrate temperature, (10) decrease in substratetemperature, (11) the use of a 12 mm focus ring, and (12) the use of a20 mm focus ring (instead of the nominal 16 mm focus ring). Each of theabove exemplary controllable process parameters are measurable andadjustable with reference to FIGS. 1 through 5. The process pressure canbe adjusted and monitored during process using either changes in, forexample, the gate valve setting or the total process gas mass flow rate,in concert with a pressure measuring device 52. The forward andreflected RF power can be adjusted and monitored using commands to theRF generator 30 (FIG. 2), the match network 32 (FIG. 2), a dualdirectional coupler (not shown) and power meters (not shown). The CF₄partial pressures can be adjusted and monitored using a mass flowcontroller to regulate the flow of CF₄ gas. The (Helium) backside gaspressure can be adjusted and monitored using backside gas deliverysystem 26, which includes a pressure regulator. In addition, thesubstrate temperature can be monitored using temperature monitoringsystem 27.

In an alternate embodiment, controllable process parameters can includea film material viscosity, a film material surface tension, an exposuretime, a depth of focus, etc.

With continuing reference to the table in FIG. 11, data can be recordedand stored digitally on controller 55 as a data matrix {overscore (X)},wherein each column in the matrix {overscore (X)} corresponds to a givenvariation in a controllable process parameter (column in the table ofFIG. 11) and each row in the matrix {overscore (X)} corresponds to aspecific spatial component. Hence, a matrix {overscore (X)} assembledfrom the data in FIG. 11 has the dimensions 16 by 12, or more generally,m by n. Once the data is stored in the matrix, the data can bemean-centered and/or normalized, if desired. Centering the data storedin a matrix column involves computing the mean value of the columnelements and subtracting it from each element. Moreover, the dataresiding in a column of the matrix can be normalized by the standarddeviation of the data in the column. The following sections will nowdiscuss the methods by which one determines the extent to whichvariations in controllable process parameters contribute to the spectralsignature of spatial components.

In order to determine the inter-relationships between variations incontrollable process parameters and the spatial components, the matrix{overscore (X)} is subject to multivariate analysis. In one embodiment,principal components analysis (PCA) is employed to derive a correlationstructure within matrix {overscore (X)} by approximating matrix X with amatrix product ({overscore (TP^(T))}) of lower dimensions plus an errormatrix {overscore (E)}, viz.{overscore (X)}={overscore (P)}{overscore (P ^(T))}+{overscore (E)},  (1)

where {overscore (T)} is a (m by p) matrix of scores that summarizes the{overscore (X)}-variables and {overscore (P)} is a (n by p, where p≦n)matrix of loadings showing the influence of the variables.

In general, the loadings matrix {overscore (P)} can be shown to comprisethe eigenvectors of the covariance matrix of {overscore (X)}, where thecovariance matrix {overscore (S)} can be shown to be{overscore (s)}={overscore (X)}^(T){overscore (X)}.   (2)

The covariance matrix {overscore (S)} is a real, symmetric matrix and,therefore, it can be described as{overscore (S)}={overscore (UΛU)}^(T),   (3)where the real, symmetric eigenvector matrix {overscore (U)} comprisesthe normalized eigenvectors as columns and {overscore (Λ)} is a diagonalmatrix comprising the eigenvalues corresponding to each eigenvectoralong the diagonal. Using equations (1) and (3) (for a full matrix ofp=n; i.e. no error matrix), one can show that{overscore (P)}={overscore (U)}   (4)and{overscore (T)}^(T){overscore (T)}={overscore (Λ)} (5)

A consequence of the above eigenanalysis is that each eigenvaluerepresents the variance of the data in the direction of thecorresponding eigenvector within n-dimensional space. Hence, the largesteigenvalue corresponds to the greatest variance in the data within then-dimensional space whereas the smallest eigenvalue represents thesmallest variance in the data. By definition, all eigenvectors areorthogonal and, therefore, the second largest eigenvalue corresponds tothe second greatest variance in the data in the direction of thecorresponding eigenvector which is, of course, normal to the directionof the first eigenvector. In general, for such analysis, the first threeto four largest eigenvalues are chosen to approximate the data and, as aresult of the approximation, an error E is introduced to therepresentation in equation (1). In summary, once the set of eigenvaluesand their corresponding eigenvectors are determined, a set of thelargest eigenvalues can be chosen and the error matrix {overscore (E)}of equation (1) can be determined.

An example of commercially available software which supports PCAmodeling is SIMCA-P 8.0; for further details, see the User's Manual(User Guide to SIMCA-P 8.0: A new standard in multivariate dataanalysis, Umetrics AB, Version 8.0, September 1999). The contents of themanual are incorporated herein by reference. Using SIMCA-P 8.0, forexample, with the data of FIG. 11, one can determine the scores matrix{overscore (T)} and the loadings matrix {overscore (P)}, as well asadditional information regarding the ability of each component todescribe each variable in {overscore (X)} and the total variation ofeach variable in {overscore (X)} by a component. FIG. 12 presents thecumulative sum of squares R2X (cum.) of all of the variables in{overscore (X)} explained by the extracted principal component(s) forthe first three principal components and the cumulative sum of the totalvariation of each variable in {overscore (X)} that can be predicted bythe extracted principal component(s) for the first three principalcomponents.

FIG. 13A presents the scores for each spatial component in t(1), t(2)space provided the exemplary data of FIG. 11 and FIG. 13B presents theloadings for each variable in p(1), p(2) space provided the exemplarydata of FIG. 11. The data of FIG. 13A, in t(1)-t(2) space, displays thedata variability through a measure of dispersion from the data centerwhere, in particular, spatial components 1 and 2 are shown to resideoutside the Hotelling T2 (5%) ellipse. This result indicates one shouldinvestigate the first and second principal components as shown in FIG.13B and should further consider also components 3 and 4. From FIG. 13B,one can derive that the variations in controllable process parametersthat would lead to a reduction in the magnitude of the spatialcomponents could potentially be increasing the cooling gas pressure(i.e. helium backside pressure), decreasing the substrate holdertemperature, decreasing the process pressure, decreasing the RF powerand utilizing a 20 mm focus ring.

Moreover, FIG. 14A presents the scores for each spatial component int(1), t(3) space provided the exemplary data of FIG. 11 and FIG. 14Bpresents the loadings for each vatiable in p(1), p(3) space provided theexemplary data of FIG. 11. A similar conclusion can be drawn fromanalysis of FIGS. 14A and 14B and, therefore, the results of thisanalysis for generating a reduction of the spatial components issummarized in the table of FIG. 15.

Utilizing the multivariate analysis summarized in FIG. 15 in conjunctionwith the data of FIG. 11, one can reduce the data set of FIG. 11 to amore manageable set of data shown in the table of FIG. 16A. From thetable of FIG. 16A and the (baseline) signature presented in FIGS. 6A,B,FIG. 16B presents a measured signature (baseline condition) and acorrection (subtracted condition) signature according to themultivariate analysis, and FIG. 16C presents the difference signatureonce the correction (subtracted) signature is removed from the measuredsignature. After adjusting the controllable process parameters followingthe guidelines of the multivariate analysis to affect the differencesignature of FIG. 16C, an improved spatial uniformity of the scan ofdata for a process performance parameter is achieved and shown relativeto the nominally measured scan of data in FIG. 17. In FIG. 17, theuniformity is improved by more than an order of magnitude (i.e.approximately 5% to 0.5%).

In an alternate embodiment, the implementation of multivariate analysisto determine a relationship between the controllable process parametersand the spatial components of process performance parameters can beachieved via design of experiment (DOE) methodologies. DOE methodologiesare well known to those skilled in the art of experiment design.

With reference now to FIG. 18A, a method of characterizing a materialprocessing system according to an embodiment of the present invention ispresented. The method 500 is described as a flow chart beginning withstep 510 in which a controllable process parameter associated with aprocess performed in the material processing system is varied. Theprocess performed in the material processing system can be, for example,the act of processing a substrate using a material processing systemsuch as, for example, one of those described in FIGS. 1 through 5. Instep 520, a scan of data, the data comprising a process performanceparameter (PPP) as discussed above (i.e. etch rate, deposition rate,etc.), at, for example, two or more points above the substrate ismeasured and recorded. In step 530, the scan of data is transformed intospectral space. In step 540, a characterization of the materialprocessing system is performed by identifying a process signature of theprocess performance parameter using one or more spatial components.Thereafter, in step 550, the process signature can be recorded in a datamatrix as, for example, a column in a data matrix.

In step 560, a decision is made as to whether an additional controllableprocess parameter should be varied. In order to further characterize thematerial processing system, steps 510 through 540 can be repeated,wherein an additional controllable process parameter associated with aprocess performed in the material processing system is varied, anadditional scan of data comprising a measurement of a processperformance parameter is measured, an additional number of spatialcomponents is determined from transforming the additional scan of data,and the material processing system is re-characterized by including anadditional process signature comprising an additional number of spatialcomponents. Furthermore, as before, the process signature can be storedin an additional column of the matrix in step 550.

In step 570, the data assembled in the data matrix can be furtherprocessed utilizing multivariate analysis in order to determineinter-relationships between the variations in the controllable processparameters and the spatial components. Examples of multivariate analysisare principal components analysis (PCA) and design of experiment (DOE),which are described above.

Referring now to FIG. 18B, a method for optinizing a process in thematerial processing system is described. In the method, a referencesignature, deemed optimum for the given process performed in thematerial processing system, can be obtained. Utilizing theinter-relationships between variations in controllable processparameters and spatial components, the process is tuned by adjusting atleast one controllable process parameter in step 610. In steps 620, 630and 640, a scan of data corresponding to a process performance parameteris measured (step 620), the scan of data is transformed into spectralspace to form a number of spatial components (step 630), and theresulting process signature is verified (step 640). In the verifyingstep 640, the process signature is assessed to determine if theoptimization of the process signature was successful. For example, ifthe optimal process is a uniform process, then the optimnized processsignature should comprise minimal amplitudes for each of its spatialcomponents. If the verifying step 640 indicates a successfuloptimization, then the multivariate analysis is not altered in step 650and a reference signature for the process in the material processingsystem is obtained in step 660. If the verifying step 640 indicates anunsuccessful optimization, then the multivariate analysis can be alteredand a series of steps described in FIG. 18A can be re-executed.

Referring now to FIG. 18C, a method 700 for improving a process in amaterial processing system is described. In step 710, a relationshipbetween the process signature and a controllable process parameter isdetermined. The relationship can be determined, for example, using datainspection, or any of the multivariate analyses described above (i.e.PCA, DOE, etc.). In step 720, a decision is made as to whether toimprove the process. The improvement can, for example, involve improvingthe uniformity of the process performance parameter. In such a case, itwould be advantageous to alter the process in order to minimize theamplitudes of at least one spatial component in the process signature,or minimize a difference signature formed from the subtraction of theprocess signature (FIG. 18A) from the reference signature (FIG. 18B). Ifno improvement is deemed necessary, all process data including theprocess recipe and process signature is recorded in step 730. If animprovement is deemed necessary, then the process is improved using avariation in at least one controllable process parameter in step 740. Instep 750, a decision is made as to whether the method should beterminated. If not, the next process (i.e. next substrate, next batch,etc.) can proceed.

In the embodiments described herein, a one-dimensional scan of data hasbeen utilized to determine a set of spatial components. In an alternateembodiment, the scan of data can be multi-dimensional such as, forexample, at least a two-dimensional scan of data.

Although only certain exemplary embodiments of this invention have beendescribed in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention.

1. A method of characterizing a material processing system, the methodcomprising the steps of: a) varying a controllable process parameterassociated with a process performed by said material processing system;b) measuring a scan of data, said scan of data comprising a measurementof a process performance parameter when said process is performed usingthe varied controllable process parameter; c) transforming said scan ofdata into a number of spatial components; and d) characterizing saidmaterial processing system by identifying a process signature, saidprocess signature comprising at least one of said spatial components. 2.The method according to claim 1, wherein the method further comprisesthe steps of: e) varying an additional controllable process parameterassociated with said process performed by said material processingsystem; f) measuring an additional scan of data, said additional scan ofdata comprising a measurement of said process performance parameter whensaid process is performed using the additional varied controllableprocess parameter; g) transforming said additional scan of data into anadditional number of spatial components; and h) re-characterizing saidmaterial processing system by including an additional process signaturecomprising said additional number of spatial components.
 3. The methodaccording to claim 2, wherein the method further comprises the step of:i) repeating step e) through step h) at least once.
 4. The methodaccording to claim 3, wherein the re-characterizing step comprisesbuilding a data matrix, wherein a first column comprises the number ofspatial components and additional columns comprise the additional numberof spatial components.
 5. The method according to claim 1, wherein themethod further comprises the steps of: e) determining a relationshipbetween said process signature and a controllable process parameter; andf) adjusting said controllable process parameter, wherein said adjustingcomprises utilizing said relationship between said signature and saidcontrollable process parameter to affect an improvement to said scan ofdata.
 6. The method according to claim 3, wherein the method furthercomprises the steps of: j) determining inter-relationships between thevariations in the controllable process parameters and the spatialcomponents using multivariate analysis; and k) adjusting at least onecontrollable process parameter, wherein said adjusting comprisesutilizing said inter-relationships to affect an improvement to saidprocess.
 7. The method according to claim 1, wherein the method furthercomprises the steps of: e) comparing said process signature with anideal signature for said process, wherein said comparing comprisesdetermining a difference signature; and f) minimizing said differencesignature by adjusting said controllable process parameter, wherein saidadjusting comprises utilizing said relationship between said signatureand said controllable process parameter.
 8. The method according toclaim 4, wherein the method further comprises the steps of: j) comparingsaid data matrix with an ideal matrix for said material processingsystem, wherein said comparing comprises determining at least onedifference signature; k) determining at least one inter-relationshipbetween a difference signature and at least one controllable processparameter; and l) minimizing said difference signature by adjusting saidat least one controllable process parameter, wherein said adjustingcomprises utilizing said at least one inter-relationship between saiddifference signature and said at least one controllable processparameter.
 9. The method according to claim 1, wherein said processcomprises processing a substrate.
 10. The method according to claim 9,wherein said substrate is at least one of a wafer or a liquid crystaldisplay.
 11. The method according to claim 1, wherein said processperformance parameter is at least one of etch rate, deposition rate,etch selectivity, etch feature anisotropy, etch feature criticaldimension, film property, plasma density, ion energy, concentration ofchemical specie, temperature, pressure, mask film thickness, and maskpattern critical dimension.
 12. The method according to claim 1, whereinsaid number of spatial components are Fourier harmonics.
 13. The methodaccording to claim 6, wherein said multivariate analysis comprisesprincipal components analysis.
 14. The method according to claim 6,wherein said multivariate analysis comprises design of experiment. 15.The method according to claim 1, wherein said controllable processparameter comprises at least one of process pressure, RF power, gas flowrate, cooling gas pressure, focus ring, electrode spacing, temperature,film material viscosity, film material surface tension, exposureintensity, and depth of focus.
 16. The method according to claim 1,wherein said scan of data is a multi-dimensional scan of data.
 17. Themethod according to claim 6, wherein said improvement comprises animprovement of a spatial uniformity of said scan of data.
 18. The methodaccording to claim 6, wherein said improvement comprises an improvementof a temporal uniformity of said scan of data.
 19. The method accordingto claim 6, wherein said improvement comprises a minimization of atleast one spatial component.
 20. A method of improving a process, themethod comprising the steps of measuring a scan of data, said scan ofdata comprising a measurement of at least one process performanceparameter, transforming said scan of data into a number of spatialcomponents, identifying a process signature, said signature comprisingat least one spatial component, determining a relationship between saidsignature and at least one controllable process parameter, said at leastone controllable process parameter being measurable during said process,and adjusting said at least one controllable process parameter, whereinsaid adjusting comprises utilizing said relationship between saidsignature and said at least one controllable process parameter to affectan improvement to said scan of data.
 21. The method according to claim20, wherein said at least one process performance parameter is at leastone of etch rate, deposition rate, etch selectivity, etch featureanisotropy, etch feature critical dimension, film property, plasmadensity, ion energy, concentration of chemical specie, temperature,pressure, mask film thickness, and mask pattern critical dimension. 22.The method according to claim 20, wherein said at least one spatialcomponent is a Fourier harmonic.
 23. The method according to claim 20,wherein said determining said relationship between said signature andsaid set of controllable process parameters comprises a multivariateanalysis.
 24. The method according to claim 23, wherein saidmultivariate analysis comprises principal components analysis.
 25. Themethod according to claim 23, wherein said multivariate analysiscomprises design of experiment.
 26. The method according to claim 20,wherein said at least one controllable process parameter comprises atleast one of process pressure, RF power, gas flow rate, cooling gaspressure, focus ring, electrode spacing, temperature, film materialviscosity, film material surface tension, exposure intensity, and depthof focus.
 27. The method according to claim 20, wherein said improvementcomprises an improvement of a spatial uniformity of said scan of data.28. The method according to claim 20, wherein said improvement comprisesa minimization of at least one spatial component.
 29. The methodaccording to claim 20, wherein said scan of data is a multi-dimensionalscan of data.
 30. The method according to claim 20, wherein saidimprovement comprises an improvement of a temporal uniformity of saidscan of data.
 31. A method of material processing, the method comprisingthe steps of performing a process, measuring a scan of data, said scanof data comprising a measurement of at least one process performanceparameter, transforming said scan of data into a number of spatialcomponents, identifying a signature of said process, said signaturecomprising at least one spatial component, determining a relationshipbetween said signature and at least one controllable process parameter,said at least one controllable process parameter being measurable duringsaid process, and adjusting said at least one controllable processparameter, wherein said adjusting comprises utilizing said relationshipbetween said signature and said at least one controllable processparameter to affect an improvement to said scan of data.
 32. The methodaccording to claim 31, wherein said performing a process comprisesprocessing a substrate.
 33. The method according to claim 32, whereinsaid substrate is at least one of a wafer and a liquid crystal display.34. The method according to claim 31, wherein said at least one processperformance parameter is at least one of etch rate, deposition rate,etch selectivity, etch feature anisotropy, etch feature criticaldimension, film property, plasma density, ion energy, concentration ofchemical specie, temperature, pressure, mask film thickness, and maskpattern critical dimension.
 35. The method according to claim 31,wherein said plurality of spatial components are Fourier harmonics. 36.The method according to claim 31, wherein said determining saidrelationship between said signature and said set of controllable processparameters comprises a multivariate analysis.
 37. The method accordingto claim 36, wherein said multivariate analysis comprises principalcomponents analysis.
 38. The method according to claim 36, wherein saidmultivariate analysis comprises design of experiment.
 39. The methodaccording to claim 31, wherein said at least one controllable processparameter comprises at least one of process pressure, RF power, gas flowrate, cooling gas pressure, focus ring, electrode spacing, temperature,film material viscosity, film material surface tension, exposureintensity, and depth of focus.
 40. The method according to claim 31,wherein said improvement comprises an improvement of a spatialuniformity of said scan of data.
 41. The method according to claim 31,wherein said improvement comprises a minimization of at least onespatial component.
 42. The method according to claim 31, wherein saidscan of data is a multi-dimensional scan of data.
 43. The methodaccording to claim 31, wherein said improvement comprises an improvementof a temporal uniformity of said scan of data.
 44. A system for materialprocessing, the system comprising process chamber, device for measuringand adjusting at least one controllable process parameter, device formeasuring at least one process performance parameter, and controller,said controller capable of performing a process, measuring a scan ofdata using said device for measuring at least one controllable processparameter, said scan of data comprising a measurement of a processperformance parameter, transforming said scan of data into a number ofspatial components, identifying a signature of said process, saidsignature comprising at least one spatial component, determining arelationship between said signature and at least one controllableprocess parameter, said at least one controllable process parameterbeing measurable during said process, and adjusting said at least onecontrollable process parameter, wherein said adjusting comprisesutilizing said relationship between said signature and said at least onecontrollable process parameter to affect an improvement to said scan ofdata.
 45. The system according to claim 44, wherein said process chamberis an etch chamber.
 46. The system according to claim 44, wherein saidprocess chamber is a deposition chamber comprising at least one ofchemical vapor deposition and physical vapor deposition.
 47. The systemaccording to claim 44, wherein said process chamber is a photoresistcoating chamber.
 48. The system according to claim 44, wherein saidprocess chamber is a dielectric coating chamber comprising at least oneof a spin-on-glass system and a spin-on-dielectric system.
 49. Thesystem according to claim 44, wherein said process chamber is aphotoresist patterning chamber.
 50. The system according to claim 49,wherein said photoresist patterning chamber is an ultravioletlithography system.
 51. The system according to claim 44, wherein saidprocess chamber is a rapid thermal processing chamber.
 52. The systemaccording to claim 44, wherein said process chamber is a batch diffusionfurnace.
 53. A system for material processing, the system comprisingprocess chamber, device for measuring and adjusting at least onecontrollable process parameter, device for measuring at least oneprocess performance parameter, and controller, said controller capableof performing a process, measuring a scan of data, said scan of datacomprising a measurement of at least one process performance parameter,transforming said scan of data into a number of spatial components,determining a signature of said process, said signature comprising atleast one spatial component, determining a relationship between saidsignature and at least one controllable process parameter, said at leastone controllable process parameter measurable during said process,comparing said signature of said process with an ideal signature forsaid process, wherein said comparing comprises determining a differencesignature, and adjusting said at least one controllable processparameter, wherein said adjusting comprises utilizing said relationshipbetween said signature and said set of controllable process parametersto affect a minimization of said difference signature.
 54. The systemaccording to claim 53, wherein said process chamber is an etch chamber.55. The system according to claim 53, wherein said process chamber is adeposition chamber comprising at least one of chemical vapor depositionand physical vapor deposition.
 56. The system according to claim 53,wherein said process chamber is a photoresist coating chamber.
 57. Thesystem according to claim 53, wherein said process chamber is adielectric coating chamber comprising at least one of a spin-on-glasssystem and a spin-on-dielectric system.
 58. The system according toclaim 53, wherein said process chamber is a photoresist patterningchamber.
 59. The system according to claim 58, wherein said photoresistpatterning chamber is an ultraviolet lithography system.
 60. The systemaccording to claim 53, wherein said process chamber is a rapid thermalprocessing chamber.
 61. The system according to claim 53, wherein saidprocess chamber is a batch diffusion furnace.